






'''simple docstring'''





from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Tuple  =					logging.get_logger(__name__)

_UpperCamelCase						:						Union[str, Any]  =					{
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"efficientformer"




					def __init__(  self  :		Optional[Any]				, _A  :		List[int] = [3, 2, 6, 4]				, _A  :		List[int] = [48, 96, 2_24, 4_48]				, _A  :		List[bool] = [True, True, True, True]				, _A  :		int = 4_48				, _A  :		int = 32				, _A  :		int = 4				, _A  :		int = 7				, _A  :		int = 5				, _A  :		int = 8				, _A  :		int = 4				, _A  :		float = 0.0				, _A  :		int = 16				, _A  :		int = 3				, _A  :		int = 3				, _A  :		int = 3				, _A  :		int = 2				, _A  :		int = 1				, _A  :		float = 0.0				, _A  :		int = 1				, _A  :		bool = True				, _A  :		bool = True				, _A  :		float = 1e-5				, _A  :		str = "gelu"				, _A  :		float = 0.02				, _A  :		float = 1e-12				, _A  :		int = 2_24				, _A  :		float = 1e-05				, **_A  :		Union[str, Any]				, )    ->							None:
								super().__init__(**_A      )

								UpperCAmelCase_      :       Dict          =       hidden_act
								UpperCAmelCase_      :       List[str]          =       hidden_dropout_prob
								UpperCAmelCase_      :       List[str]          =       hidden_sizes
								UpperCAmelCase_      :       int          =       num_hidden_layers
								UpperCAmelCase_      :       List[str]          =       num_attention_heads
								UpperCAmelCase_      :       Dict          =       initializer_range
								UpperCAmelCase_      :       Dict          =       layer_norm_eps
								UpperCAmelCase_      :       Optional[int]          =       patch_size
								UpperCAmelCase_      :       Any          =       num_channels
								UpperCAmelCase_      :       Any          =       depths
								UpperCAmelCase_      :       Optional[int]          =       mlp_expansion_ratio
								UpperCAmelCase_      :       Union[str, Any]          =       downsamples
								UpperCAmelCase_      :       Any          =       dim
								UpperCAmelCase_      :       Optional[int]          =       key_dim
								UpperCAmelCase_      :       int          =       attention_ratio
								UpperCAmelCase_      :       Union[str, Any]          =       resolution
								UpperCAmelCase_      :       Union[str, Any]          =       pool_size
								UpperCAmelCase_      :       Dict          =       downsample_patch_size
								UpperCAmelCase_      :       int          =       downsample_stride
								UpperCAmelCase_      :       Optional[Any]          =       downsample_pad
								UpperCAmelCase_      :       Union[str, Any]          =       drop_path_rate
								UpperCAmelCase_      :       Optional[Any]          =       num_metaad_blocks
								UpperCAmelCase_      :       Optional[int]          =       distillation
								UpperCAmelCase_      :       Union[str, Any]          =       use_layer_scale
								UpperCAmelCase_      :       Optional[int]          =       layer_scale_init_value
								UpperCAmelCase_      :       str          =       image_size
								UpperCAmelCase_      :       str          =       batch_norm_eps










'''simple docstring'''





from unittest.mock import patch

import pyspark

from datasets.packaged_modules.spark.spark import (
    Spark,
    SparkExamplesIterable,
    _generate_iterable_examples,
)

from ..utils import (
    require_dill_gt_0_3_2,
    require_not_windows,
)
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Optional[int]		)    ->   str:
			UpperCAmelCase_      :       List[Any]          =       []
			for part_id in partition_order:
						UpperCAmelCase_      :       Any          =       df.where(F"SPARK_PARTITION_ID() = {part_id}"		).collect()
						for row_idx, row in enumerate(A		):
									expected_row_ids_and_row_dicts.append((F"{part_id}_{row_idx}", row.asDict())		)
			return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       Any          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       List[Any]          =       Spark(A		)
			# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
			# that each partition can hold 2 rows.
			spark_builder._repartition_df_if_needed(max_shard_size=1_6		)
			# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
			assert spark_builder.df.rdd.getNumPartitions() == 5_0
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       List[str]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Optional[Any]          =       spark.range(1_0		).repartition(2		)
			UpperCAmelCase_      :       int          =       [1, 0]
			UpperCAmelCase_      :       str          =       _generate_iterable_examples(A			,       A		)  # Reverse the partitions.
			UpperCAmelCase_      :       Optional[Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       A		)

			for i, (row_id, row_dict) in enumerate(generate_fn()		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       expected_row_ids_and_row_dicts[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       str          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[Any]          =       spark.range(1_0		).repartition(1		)
			UpperCAmelCase_      :       Optional[int]          =       SparkExamplesIterable(A		)
			assert it.n_shards == 1
			for i, (row_id, row_dict) in enumerate(A		):
						assert row_id == F"0_{i}"
						assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Union[str, Any]:
			UpperCAmelCase_      :       Dict          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       Dict          =       spark.range(3_0		).repartition(3		)
			# Mock the generator so that shuffle reverses the partition indices.
			with patch('''numpy.random.Generator'''		) as generator_mock:
						UpperCAmelCase_      :       Any          =       lambda A		: x.reverse()
						UpperCAmelCase_      :       Dict          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [2, 1, 0]		)

						UpperCAmelCase_      :       List[Any]          =       SparkExamplesIterable(A		).shuffle_data_sources(A		)
						assert shuffled_it.n_shards == 3
						for i, (row_id, row_dict) in enumerate(A		):
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts[i]
									assert row_id == expected_row_id
									assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   int:
			UpperCAmelCase_      :       Tuple          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       int          =       spark.range(2_0		).repartition(4		)

			# Partitions 0 and 2
			UpperCAmelCase_      :       int          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=0			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       Union[str, Any]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [0, 2]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict

			# Partitions 1 and 3
			UpperCAmelCase_      :       Tuple          =       SparkExamplesIterable(A		).shard_data_sources(worker_id=1			,       num_workers=2		)
			assert shard_it_a.n_shards == 2
			UpperCAmelCase_      :       List[str]          =       _get_expected_row_ids_and_row_dicts_for_partition_order(A			,       [1, 3]		)
			for i, (row_id, row_dict) in enumerate(A		):
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Union[str, Any]          =       expected_row_ids_and_row_dicts_a[i]
						assert row_id == expected_row_id
						assert row_dict == expected_row_dict







@require_not_windows
@require_dill_gt_0_3_2
def __UpperCAmelCase   (					)    ->   Any:
			UpperCAmelCase_      :       List[Any]          =       pyspark.sql.SparkSession.builder.master('''local[*]'''		).appName('''pyspark'''		).getOrCreate()
			UpperCAmelCase_      :       List[str]          =       spark.range(1_0_0		).repartition(1		)
			UpperCAmelCase_      :       int          =       Spark(A		)
			# Choose a small max_shard_size for maximum partitioning.
			spark_builder._repartition_df_if_needed(max_shard_size=1		)
			# The new number of partitions should not be greater than the number of rows.
			assert spark_builder.df.rdd.getNumPartitions() == 1_0_0


